1. Primary Dimension

This leads to the first way of approaching a dimension. It’s the default situation if you click on one of the reports in Google Analytics.

In the screenshot shown above “source/medium” is the primary dimension.

Why to use it:

Primary dimensions provide you with high-level insights in how a dimension performs in relation to a set of metrics. In this example it shows you how the different traffic sources perform against a standard set of ABC metrics.

2. Secondary Dimension

Let’s go one level deeper. You can also build a report where “source/medium” is applied as the secondary dimension:

Do you see what has happened? I have chosen “landing page” as the primary dimension. “Source/medium” is now the secondary dimension.

Why to use it:

Secondary dimensions provide you with a unique way to cross-segment one dimension against another. In this example it shows you how the different landing pages perform in relation to the traffic sources (defined as “source/medium”).

Why to use it:

Tertiary dimensions provide you with a unique way to double cross-segment one dimension against two others. It’s especially useful if you have a large data set and you want to dive deeper into one value. In this example it shows you how one landing page performs in relation to operating system of user and source/medium (where the user came from).

Tip: you can add a filter on a particular “source/medium” if you only want to show the data related to that segment.

4. Dimension in Custom Segment

Google Analytics does a tremendous job in helping you analyze segments of Google Analytics data on the fly.

In our example we looked at “source/medium”.

You can filter most report data on only one “source/medium” by applying one segment.

I have built a segment on “google/organic”:

If you apply this segment to your data, your reports will be fully focused on this traffic source.

Here is an example report:

Why to use it:

Segments offer a tremendous opportunity to view one dimension and a specific value in context of many different reports. In this case I have segmented on “google/organic” and can view all reports for this dimension. It delivers me a great deal of information on how this particular dimension performs against many other metrics and dimensions.

5. Multiple Dimensions in Custom Segment

Besides segmenting on one single dimension, you can combine multiple segments in one analysis.

Let’s assume you would like to combine the following dimensions:

Source / medium = google / organic

Country = United States

Type of visitors = New

It’s easy to set this up:

After you have applied this segment in the reporting interface, you can view the three different reports that are connected to each of your segments:

This ensures your segment set up is done in the right way.

I have already shown you the All Pages report with one dimension. Here is the same report with the new segment:

Why to use it:

Combined segments bring even more context to all the different reports you take a look at. In this case I have segmented on “google/organic”, “United States” and “New”. All reports are now focused around the combination of these dimensions. It delivers me a great deal of information on how a micro-segment performs.

6. Dimension and Custom Report

Custom reports come in scope if you want to review one or more dimensions in context of a chosen set of metrics.

You can deviate from the default report settings here.

Two options:

Combine multiple metrics from different report tabs.

Come up with a complete new metrics reporting structure.

As an example please see the custom report (flat table) set up below:

And this is how the actual report looks like:

Why to use it:

Custom reports with one dimension provide you with a lot of flexibility. You can combine one dimension or micro-dimension with your favorite metrics or KPIs. You can also change the order in which the different metrics are displayed. This makes it easy to evaluate or monitor one dimension without having to view many different reports.

7. Multiple Dimensions and Custom Report

I like to share one additional way to work with dimensions in Google Analytics: multiple dimensions combined in a custom report.

In order to get this to work you need to use a flat table. Within a flat table you are able to select up to five dimensions.

Here is an example set up with three different dimensions:

The actual report looks like this:

Working with multiple dimensions and a flat able can work, but visually it doesn’t look very appealing.

Why to use it:

Custom reports with multiple dimensions can provide you with interesting insights on a micro-segment level. You have a lot of flexibility on which metrics and dimensions you include and in what order. If you want to, you can even pre-filter you data on one or more dimensions.

Reader Interactions

Comments

Hi Paul
Very usefull like usually!
I’ve been working tracking events and I found that when adding a secondary dimension the total number of unique events is less than without a secondary dimension (in this case source)

Thanks for your comment and I am glad this article is useful.
I have checked your finding in different accounts and could not reproduce it.
First I thought about sampling, but it seems sampling already occurs at the overall level in the reporting view for events.

One thing you might want to check: set the “Control the number of sessions used to calculate this report:” to highest precision. You can do this in the top right of the screen. And make sure to also have this selected when you apply the secondary dimension.

If this doesn’t help, I recommend to add a comment in the Google+ Google Analytics community. Someone should know the answer!

hi poul
your content is just make me happy every time:)
I have a question about using segment and dimension
In tip number 6 when you use custom segment and use three dimension, can we use primary and secondary dimension as well?
and now drill down even more?

That’s great to hear Afshin! :-) Yes, you can combine multiple dimensions and apply a segment as well. However, keep in mind that “how to interpret the data” becomes more difficult when applying segments.